SoroushVahidi/scidocs huggingface dataset:
Assessing the effects of service quality and justice on customer satisfaction and the continuance intention of mobile value-added services: An empirical test of a multidimensional model
(2012)

a r t i c l e i n f o Understanding the antecedents and consequences of customer satisfaction in the mobile communications market is important. This study explores the effects of service quality and justice on customer satisfaction, which, in turn, affects continuance intention of mobile services. Service quality, justice and customer satisfaction were measured by multiple dimensions. A research model was developed based on this multidimen-sional approach and was empirically examined with data collected from about one thousand users of mobile value-added services in China. Results show that all three dimensions of service quality (interaction quality, environment quality and outcome quality) have significant and positive effects on cumulative satisfaction while only one dimension of service quality (interaction quality) has a significant and positive effect on transaction-specific satisfaction. Besides procedural justice, the other two dimensions of justice (distribu-tive justice and interactional justice) significantly influence both transaction-specific satisfaction and cumulative satisfaction. Furthermore, both types of customer satisfaction have significant and positive effects on continuance intention. Implications for research and practice are discussed. With the rapid advancements of mobile network technologies, provision of various kinds of value-added services by mobile service providers is on the rise around the world. As the market becomes more and more mature, value-added services become more homogeneous and the competition for acquiring new customers and retaining existing customers becomes more intense. In this environment, customer satisfaction is a critical factor for mobile service providers to maintain or improve their market share and profitability. Prior studies have found that customer satisfaction contributes to a firm's profitability and customer retention [33,35]. In a reorganization of the communications industry in China between 2008 and 2009, the original six mobile network operators were reduced to three. Meanwhile, the availability of third-generation telecommunications technologies suggested that more mobile value-added services would be provided to the customers. A recent value-added services survey report on mobile communications conducted by Analysys in 2010 predicted that, the competition among existing mobile network operators would become fierce after the reorganization of the industry and the introduction of third-generation services. Thus, for these mobile network operators, in order to retain customers, enhancing customer satisfaction is an urgent task to tackle with. Moreover, as new mobile value-added services are released, service providers need to focus on if these new services appeal to customers and on the willingness of customers to continue to use the services. Therefore, understanding the …
A Low-Noise Amplifier With Tunable Interference Rejection for 3.1- to 10.6-GHz UWB Systems
(2010)

An ultrawideband common-gate low noise amplifier with tunable interference rejection is presented. The proposed LNA embeds a tunable active notch filter to eliminate interferer at 5-GHz WLAN and employs a common-gate input stage and dual-resonant loads for wideband implementation. This LNA has been fabricated in a 0.18-¿m CMOS process. The measured maximum power gain is 13.2 dB and noise figure is 4.5-6.2 dB with bandwidth of 3.1-10.6 GHz. The interferer rejection is 8.2 dB compared to the maximum gain and 7.6 dB noise figure at 5.2 GHz , respectively. The measured input P1dB is -11 dBm at 10.3 GHz. It consumes 12.8 mA from 1.8-V supply voltage.
A strategic framework for good governance through e-governance optimization: A case study of Punjab in India
(2015)

Purpose – The purpose of this paper is to attempt to find out whether the new information and communication technologies can make a significant contribution to the achievement of the objective of good governance. The study identifies the factors responsible for creating a conducive environment for effective and successful implementation of e-governance for achieving good governance and the possible barriers in the implementation of e governance applications. Based on the comprehensive analysis it proposes a strategic policy framework for good governance in Punjab in India. Punjab is a developed state ranked amongst some of the top states of India in terms of per capita income and infrastructure. Design/methodology/approach – The study designs a framework for good governance by getting the shared vision of all stakeholders about providing good quality administration and governance in the Indian context through “Participatory Stakeholder Assessment”. The study uses descriptive statistics, perception gap, ANOVA and factor analysis to identify the key factors for good governance, the priorities of public regarding e-services, the policy makers’ perspectives regarding good governance to be achieved through e-governance. Findings – The study captures the good governance factors mainly contributing to the shared vision. The study further highlights that most Indian citizens in Punjab today believe in the power of information and communication technology (ICT) and want to access e-governance services. Major factors causing pain and harassment to the citizens in getting the services from various government departments include: unreasonable delay, multiple visits even for small services; poor public infrastructure and its maintenance in government offices. In the understanding of citizens the most important factors for the success of e-governance services are: overall convenience and experience of the citizens; reduction in the corruption levels by improvement in the transparency of government functioning and awareness about the availability of service amongst general masses. Originality/value – The present study has evolved a shared vision of all stakeholders on good governance in the Indian context. It has opened up many new possibilities for the governments, not only to use ICTs and help them in prioritizing the governance areas for focused attention, but also help to understand the mindset of the modern citizenry, their priorities and what they consider as good governance. The study will help policy makers focus on these factors for enhancing speedy delivery of prioritized services and promote good governance in developing countries similar to India.
Deanonymizing mobility traces: using social network as a side-channel
(2012)

Location-based services, which employ data from smartphones, vehicles, etc., are growing in popularity. To reduce the threat that shared location data poses to a user's privacy, some services anonymize or obfuscate this data. In this paper, we show these methods can be effectively defeated: a set of location traces can be deanonymized given an easily obtained social network graph. The key idea of our approach is that a user may be identified by those she meets: a "contact graph" identifying meetings between anonymized users in a set of traces can be structurally correlated with a social network graph, thereby identifying anonymized users. We demonstrate the effectiveness of our approach using three real world datasets: University of St Andrews mobility trace and social network (27 nodes each), SmallBlue contact trace and Facebook social network (125 nodes), and Infocom 2006 bluetooth contact traces and conference attendees' DBLP social network (78 nodes). Our experiments show that 80% of users are identified precisely, while only 8% are identified incorrectly, with the remainder mapped to a small set of users.
A topology control approach for utilizing multiple channels in multi-radio wireless mesh networks
(2005)

We consider the channel assignment problem in a multi-radio wireless mesh network that involves assigning channels to radio interfaces for achieving efficient channel utilization. We propose the notion of a traffic-independent base channel assignment to ease coordination and enable dynamic, efficient and flexible channel assignment. We present a novel formulation of the base channel assignment as a topology control problem, and show that the resulting optimization problem is NP-complete. We then develop a new greedy heuristic channel assignment algorithm (termed CLICA) for finding connected, low interference topologies by utilizing multiple channels. Our extensive simulation studies show that the proposed CLICA algorithm can provide large reduction in interference (even with a small number of radios per node), which in turn leads to significant gains in both link layer and multihop performance in 802.11-based multi-radio mesh networks.
Pattern frequency representation for time series classification
(2016)

The paper presents a new method for data transformation. The obtained data format enhances the efficiency of the time series classification. The transformation is realized in four steps, namely: time series segmentation, segment's feature representation, segments' binning and pattern frequency representation. The method is independent of the nature of the data, and it works well with temporal data from diverse areas. Its ability is proved by classification of different real datasets and is also compared with the results of other classification methods.
Unsupervised document classification using sequential information maximization
(2002)

We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential (sIB) approach is guaranteed to converge to a local maximum of the information with time and space complexity typically linear in the data size. information, as required by the original IB principle. Moreover, the time and space complexity are significantly improved. We apply this algorithm to unsupervised document classification. In our evaluation, on small and medium size corpora, the sIB is found to be consistently superior to all the other clustering methods we examine, typically by a significant margin. Moreover, the sIB results are comparable to those obtained by a supervised Naive Bayes classifier. Finally, we propose a simple procedure for trading cluster's recall to gain higher precision, and show how this approach can extract clusters which match the existing topics of the corpus almost perfectly.
Contextual RNN-GANs for Abstract Reasoning Diagram Generation
(2016)

Education University of California, Berkeley (2008-2013) Ph.D. in Computer Science Thesis: Surface Web Semantics for Structured Natural Language Processing Advisor: Dan Klein. Committee members: Dan Klein, Marti Hearst, Line Mikkelsen, Nelson Morgan University of California, Berkeley (2012) Master of Science (M.S.) in Computer Science Thesis: An All-Fragments Grammar for Simple and Accurate Parsing Advisor: Dan Klein Indian Institute of Technology, Kanpur (2004-2008) Bachelor of Technology (B.Tech.) in Computer Science and Engineering GPA: 3.96/4.00 (Institute and Department Rank 2) Cornell University (Summer 2007) CS490 (Independent Research and Reading) GPA: 4.00/4.00 Advisors: Lillian Lee, Claire Cardie
An Energy-Efficient and Wide-Range Voltage Level Shifter With Dual Current Mirror
(2017)

This brief presents an energy-efficient level shifter (LS) to convert a subthreshold input signal to an above-threshold output signal. In order to achieve a wide range of conversion, a dual current mirror (CM) structure consisting of a virtual CM and an auxiliary CM is proposed. The circuit has been implemented and optimized in SMIC 40-nm technology. The postlayout simulation demonstrates that the new LS can achieve voltage conversion from 0.2 to 1.1 V. Moreover, at the target voltage of 0.3 V, the proposed LS exhibits an average propagation delay of 66.48 ns, a total energy per transition of 72.31 fJ, and a static power consumption of 88.4 pW, demonstrating an improvement of <inline-formula> <tex-math notation="LaTeX">$6.0\times $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$13.1\times $ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$89.0\times $ </tex-math></inline-formula>, respectively, when compared with Wilson CM-based LS.
A 60 V Auto-Zero and Chopper Operational Amplifier With 800 kHz Interleaved Clocks and Input Bias Current Trimming
(2015)

An auto-zero and chopper operational amplifier with a 4.5-60 V supply voltage range is realized, using a 0.18 μm CMOS process augmented by 5 V CMOS and 60 V DMOS transistors. It achieves a maximum offset voltage drift of 0.02 μV/°C, a minimum CMRR of 145 dB, a noise PSD of 6.8 nV/√Hz, and a 3.1 MHz unity gain bandwidth, while dissipating 840 μA of current. Up-modulated chopper ripple is suppressed by auto- zeroing. Furthermore, glitches from the charge injection of the input switches are mitigated by employing six parallel input stages with 800 kHz interleaved clocks. This moves the majority of the glitch energy up to 4.8 MHz, while leaving little energy at 800 kHz. As a result, the requirements on an external low-pass glitch filter is relaxed, and a wider usable signal bandwidth can be obtained. Maximum input bias current due to charge injection mismatch is reduced from 1.5 nA to 150 pA by post production trimming with an on-chip charge mismatch compensation circuit.
Laser hair removal.
(2011)

The extended theory of selective photothermolysis enables the laser surgeon to target and destroy hair follicles, thereby leading to hair removal. Today, laser hair removal (LHR) is the most commonly requested cosmetic procedure in the world and is routinely performed by dermatologists, other physicians, and non-physician personnel with variable efficacy. The ideal candidate for LHR is fair skinned with dark terminal hair; however, LHR can today be successfully performed in all skin types. Knowledge of hair follicle anatomy and physiology, proper patient selection and preoperative preparation, principles of laser safety, familiarity with the various laser/light devices, and a thorough understanding of laser-tissue interactions are vital to optimizing treatment efficacy while minimizing complications and side effects.
Psychological Well-Being in Adult Life
(2008)

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ENN: Extended Nearest Neighbor Method for Pattern Recognition [Research Frontier]
(2015)

This article introduces a new supervised classification method - the extended nearest neighbor (ENN) - that predicts input patterns according to the maximum gain of intra-class coherence. Unlike the classic k-nearest neighbor (KNN) method, in which only the nearest neighbors of a test sample are used to estimate a group membership, the ENN method makes a prediction in a "two-way communication" style: it considers not only who are the nearest neighbors of the test sample, but also who consider the test sample as their nearest neighbors. By exploiting the generalized class-wise statistics from all training data by iteratively assuming all the possible class memberships of a test sample, the ENN is able to learn from the global distribution, therefore improving pattern recognition performance and providing a powerful technique for a wide range of data analysis applications.
Fast and Accurate Template Matching Using Pixel Rearrangement on the GPU
(2011)

A GPU (Graphics Processing Unit) is a specialized processor for graphics processing. GPUs have the ability to perform high-speed parallel processing using its many processing cores. To utilize the powerful computing ability, GPUs are widely used for general purpose processing. The main contribution of this paper is to show a new template matching algorithm using pixel rearrangement. Template Matching is a technique for finding small parts of an image which match a template image. The feature of our proposed algorithm is that using pixel rearrangement, multiple low-resolution images are generated and template matching for the low-resolution images is performed to reduce the computing time. Also, we implemented our algorithm on a GPU system. The experimental results show that, for an input image with size of 4096 $\times$ 4096 and a template image with size of 256 $\times$ 256, our implementation can achieve a speedup factor of approximately 78 times over the conventional sequential implementation.
Visual Digital Signature Scheme: A New Approach
(2010)

—A digital signature is an important public-key primitive that performs the function of conventional handwritten signatures for entity authentication, data integrity, and non-repudiation, especially within the electronic commerce environment. Currently, most conventional digital signature schemes are based on mathematical hard problems. These mathematical algorithms require computers to perform the heavy and complex computations to generate and verify the keys and signatures. In 1995, Naor and Shamir proposed a visual cryptography (VC) for binary images. VC has high security and requires simple computations. The purpose of this paper is to provide an alternative to the current digital signature technology. In this paper, we introduce a new digital signature scheme based on the concept of a non-expansion visual cryptography. A visual digital signature scheme is a method to enable visual verification of the authenticity of an image in an insecure environment without the need to perform any complex computations. Our proposed scheme generates visual shares and manipulates them using the simple Boolean operations OR rather than generating and computing large and long random integer values as in the conventional digital signature schemes currently in use.
Analyzing Stability in Wide-Area Network Performance
(1997)

The Internet is a very large scale, complex, dynamical system that is hard to model and analyze. In this paper, we develop and analyze statistical models for the observed end-to-end network performance based on extensive packet-level traces (consisting of approximately 1.5 billion packets) collected from the primary Web site for the Atlanta Summer Olympic Games in 1996. We find that observed mean throughputs for these transfers measured over 60 million complete connections vary widely as a function of end-host location and time of day, confirming that the Internet is characterized by a large degree of heterogeneity. Despite this heterogeneity, we find (using best-fit linear regression techniques) that we can express the throughput for Web transfers to most hosts as a random variable with a log-normal distribution. Then, using observed throughput as the control parameter, we attempt to quantify the spatial (statistical similarity across neighboring hosts) and temporal (persistence over time) stability of network performance. We find that Internet hosts that are close to each other often have almost identically distributed probability distributions of throughput. We also find that throughputs to individual hosts often do not change appreciably for several minutes. Overall, these results indicate that there is promise in protocol mechanisms that cache and share network characteristics both within a single host and amongst nearby hosts.
Learning to Grade Short Answer Questions using Semantic Similarity Measures and Dependency Graph Alignments
(2011)

In this work we address the task of computerassisted assessment of short student answers. We combine several graph alignment features with lexical semantic similarity measures using machine learning techniques and show that the student answers can be more accurately graded than if the semantic measures were used in isolation. We also present a first attempt to align the dependency graphs of the student and the instructor answers in order to make use of a structural component in the automatic grading of student answers.
Tuned Models of Peer Assessment in MOOCs
(2013)

In massive open-access online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students. But despite promising initial trials, it does not always deliver accurate results compared to human experts. In this paper, we develop algorithms for estimating and correcting for grader biases and reliabilities, showing significant improvement in peer grading accuracy on real data with 63,199 peer grades from Coursera’s HCI course offerings — the largest peer grading networks analysed to date. We relate grader biases and reliabilities to other student factors such as engagement, performance as well as commenting style. We also show that our model can lead to more intelligent assignment of graders to gradees.
The critical importance of retrieval for learning.
(2008)

Learning is often considered complete when a student can produce the correct answer to a question. In our research, students in one condition learned foreign language vocabulary words in the standard paradigm of repeated study-test trials. In three other conditions, once a student had correctly produced the vocabulary item, it was repeatedly studied but dropped from further testing, repeatedly tested but dropped from further study, or dropped from both study and test. Repeated studying after learning had no effect on delayed recall, but repeated testing produced a large positive effect. In addition, students' predictions of their performance were uncorrelated with actual performance. The results demonstrate the critical role of retrieval practice in consolidating learning and show that even university students seem unaware of this fact.
Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading
(2013)

We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as “powergrading.” We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. We show results in terms of grading progress with a small “budget” of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.
EARLY DEVELOPMENTS OF A PARALLELLY ACTUATED HUMANOID , SAFFIR
(2013)

This paper presents the design of our new 33 degree of freedom full size humanoid robot, SAFFiR (Shipboard Autonomous Fire Fighting Robot). The goal of this research project is to realize a high performance mixed force and position controlled robot with parallel actuation. The robot has two 6 DOF legs and arms, a waist, neck, and 3 DOF hands/fingers. The design is characterized by a central lightweight skeleton actuated with modular ballscrew driven force controllable linear actuators arranged in a parallel fashion around the joints. Sensory feedback on board the robot includes an inertial measurement unit, force and position output of each actuator, as well as 6 axis force/torque measurements from the feet. The lower body of the robot has been fabricated and a rudimentary walking algorithm implemented while the upper body fabrication is completed. Preliminary walking experiments show that parallel actuation successfully minimizes the loads through individual actuators.